Machine Learning in Ocular Oncology and Oculoplasty: Transforming Diagnosis and Treatment

Abstract

In ocular oncology and oculoplasty, machine learning (ML) has become a game-changing technology, providing previously unheard-of levels of precision in diagnosis, treatment planning, and outcome prediction. Using imaging modalities, genomic data, and clinical characteristics, this chapter investigates the integration of machine learning algorithms in detecting and treating ocular tumors, including retinoblastoma and uveal melanoma. Predictive modeling and real-time decision-making also emphasize how ML might improve surgical outcomes in oculoplasty, including orbital reconstruction and eyelid correction. Automated examination of fundus photographs, histological slides, and 3D imaging has been made possible by deep learning and natural language processing, improving individualized therapeutic approaches and decreasing diagnostic errors. Additionally, using augmented reality and machine learning in robotics and surgery is a significant development in precision oculoplasty. Notwithstanding its potential, issues including data heterogeneity, algorithm interpretability, and ethical considerations are significant roadblocks that must be addressed. This chapter explores cutting-edge developments, real-world uses, and potential future paths, offering researchers and doctors a thorough resource.